title: Missing data handling in chronic pain trials.
abstract: In chronic pain trials, proper handling of missing data due to dropout is an important issue because the dropout rate is high and the study conclusion may depend on the method chosen. The intent-to-treat (ITT) principle usually requires imputations for missing data to include the dropouts as well as completers in the statistical analysis. However, a statistical analysis with imputation might lead to a misinterpretation of clinical data. In chronic pain trials, treatment-related dropouts are clinical outcomes themselves. For example, an early dropout due to toxicity usually indicates a treatment failure, as does a dropout due to lack of efficacy. Problems with traditional methods such as last observation carried forward (LOCF) or baseline observation carried forward (BOCF) are identified especially in the chronic pain setting. Alternative methods, such as continuous responder analysis and two-part model analysis, treating dropouts as clinical events, are introduced with an example of osteoarthritis clinical trial data.
authors.affiliation: Food and Drug Administration, Silver Spring, Maryland, USA. yongman.kim@fda.hhs.gov
authors.forename: Yongman
authors.initials: Y
authors.lastname: Kim
mesh_terms: D000700:Analgesics / Q000009:adverse effects* / Q000627:therapeutic use*; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D006801:Humans; D057194:Intention to Treat Analysis / Q000379:methods; D015233:Models, Statistical; D010146:Pain / Q000188:drug therapy*; D059408:Pain Management; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic; D016896:Treatment Outcome
chemical_list: D000700:Analgesics